In [40]:
image_path = "/content/runs/detect/train/train_batch0.jpg"
display(Image.open(image_path))
Universidad EAN
Gerencia de Proyectos
Encuentro Final
Profesor:
Carlos Isaac Zainea
Preparado por
Andrés Felipe Sainea Rojas
Laura Carolina Mateus Agudelo
Daniel Antonio Pérez Beltrán
Febrero 28, 2025
Instalación de paquetes, importar las librerías necesarias para la ejecución del notebook
import gdown
import os
import torch
import yaml
import numpy as np
import shutil
import matplotlib.pyplot as plt
import seaborn as sns
from ultralytics import YOLO
from IPython.display import display
from PIL import Image
from sklearn.metrics import roc_curve, auc
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fsspec in /usr/local/lib/python3.11/dist-packages (from torch) (2024.10.0) Collecting nvidia-cuda-nvrtc-cu12==12.4.127 (from torch) Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cuda-runtime-cu12==12.4.127 (from torch) Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cuda-cupti-cu12==12.4.127 (from torch) Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cudnn-cu12==9.1.0.70 (from torch) Downloading nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cublas-cu12==12.4.5.8 (from torch) Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cufft-cu12==11.2.1.3 (from torch) Downloading nvidia_cufft_cu12-11.2.1.3-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-curand-cu12==10.3.5.147 (from torch) Downloading nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Collecting nvidia-cusolver-cu12==11.6.1.9 (from torch) Downloading nvidia_cusolver_cu12-11.6.1.9-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Collecting nvidia-cusparse-cu12==12.3.1.170 (from torch) Downloading nvidia_cusparse_cu12-12.3.1.170-py3-none-manylinux2014_x86_64.whl.metadata (1.6 kB) Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch) (2.21.5) Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch) (12.4.127) Collecting nvidia-nvjitlink-cu12==12.4.127 (from torch) Downloading nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB) Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch) (3.1.0) Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch) (1.3.0) Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch) (3.0.2) Downloading nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_x86_64.whl (363.4 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 363.4/363.4 MB 2.9 MB/s eta 0:00:00 Downloading nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (13.8 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 13.8/13.8 MB 81.7 MB/s eta 0:00:00 Downloading nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (24.6 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 24.6/24.6 MB 82.5 MB/s eta 0:00:00 Downloading nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (883 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 883.7/883.7 kB 44.1 MB/s eta 0:00:00 Downloading 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nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12 Attempting uninstall: nvidia-nvjitlink-cu12 Found existing installation: nvidia-nvjitlink-cu12 12.5.82 Uninstalling nvidia-nvjitlink-cu12-12.5.82: Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82 Attempting uninstall: nvidia-curand-cu12 Found existing installation: nvidia-curand-cu12 10.3.6.82 Uninstalling nvidia-curand-cu12-10.3.6.82: Successfully uninstalled nvidia-curand-cu12-10.3.6.82 Attempting uninstall: nvidia-cufft-cu12 Found existing installation: nvidia-cufft-cu12 11.2.3.61 Uninstalling nvidia-cufft-cu12-11.2.3.61: Successfully uninstalled nvidia-cufft-cu12-11.2.3.61 Attempting uninstall: nvidia-cuda-runtime-cu12 Found existing installation: nvidia-cuda-runtime-cu12 12.5.82 Uninstalling nvidia-cuda-runtime-cu12-12.5.82: Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82 Attempting uninstall: nvidia-cuda-nvrtc-cu12 Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82 Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82: Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82 Attempting uninstall: nvidia-cuda-cupti-cu12 Found existing installation: nvidia-cuda-cupti-cu12 12.5.82 Uninstalling nvidia-cuda-cupti-cu12-12.5.82: Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82 Attempting uninstall: nvidia-cublas-cu12 Found existing installation: nvidia-cublas-cu12 12.5.3.2 Uninstalling nvidia-cublas-cu12-12.5.3.2: Successfully uninstalled nvidia-cublas-cu12-12.5.3.2 Attempting uninstall: nvidia-cusparse-cu12 Found existing installation: nvidia-cusparse-cu12 12.5.1.3 Uninstalling nvidia-cusparse-cu12-12.5.1.3: Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3 Attempting uninstall: nvidia-cudnn-cu12 Found existing installation: nvidia-cudnn-cu12 9.3.0.75 Uninstalling nvidia-cudnn-cu12-9.3.0.75: Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75 Attempting uninstall: nvidia-cusolver-cu12 Found existing installation: nvidia-cusolver-cu12 11.6.3.83 Uninstalling nvidia-cusolver-cu12-11.6.3.83: Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83 Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127 Collecting ultralytics Downloading ultralytics-8.3.81-py3-none-any.whl.metadata (35 kB) Requirement already satisfied: numpy<=2.1.1,>=1.23.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (1.26.4) Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (3.10.0) Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.11/dist-packages (from 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py-cpuinfo in /usr/local/lib/python3.11/dist-packages (from ultralytics) (9.0.0) Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (2.2.2) Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.11/dist-packages (from ultralytics) (0.13.2) Collecting ultralytics-thop>=2.0.0 (from ultralytics) Downloading ultralytics_thop-2.0.14-py3-none-any.whl.metadata (9.4 kB) Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.1) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1) Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.56.0) Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.8) Requirement 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torch>=1.8.0->ultralytics) (12.4.127) Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127) Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127) Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (9.1.0.70) Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.5.8) Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (11.2.1.3) Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (10.3.5.147) Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (11.6.1.9) Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.3.1.170) Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (2.21.5) Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127) Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (12.4.127) Requirement already satisfied: triton==3.1.0 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (3.1.0) Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.11/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.11/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0) Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.11/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2) Downloading ultralytics-8.3.81-py3-none-any.whl (922 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 922.1/922.1 kB 43.1 MB/s eta 0:00:00 Downloading ultralytics_thop-2.0.14-py3-none-any.whl (26 kB) Installing collected packages: ultralytics-thop, ultralytics Successfully installed ultralytics-8.3.81 ultralytics-thop-2.0.14 Creating new Ultralytics Settings v0.0.6 file ✅ View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json' Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.
Para usar este modelo se requieren imágenes que son descargadas mediante una búsqueda en Google Images. Para ello se han descargado y etiquetado 7000 imágenes. Las imágenes se cargan a la ruta /content/ en el archivo Dataset.zip, el cual será descomprimido para hacer uso de las imágenes.
El folder del dataset debe estar estructurado otros folders que serán usados como datos de entrenamiento, prueba y validación:
file_id = "1lMC00LhK1HMoERpySn5BR5V0U9jLl-hf"
output_path = "/content/datasets.zip"
# Descargar con gdown
gdown.download(f"https://drive.google.com/uc?id={file_id}", output_path, quiet=False)
# Verificar si el archivo es un ZIP válido antes de descomprimir
if os.path.exists(output_path) and os.path.getsize(output_path) > 1000:
print("✅ Archivo descargado con éxito. Procediendo a descomprimir...")
!unzip -q "/content/datasets.zip" -d "/content/datasets"
else:
print("❌ Error: El archivo no se descargó correctamente. Verifica el enlace en Google Drive.")
Downloading... From (original): https://drive.google.com/uc?id=1lMC00LhK1HMoERpySn5BR5V0U9jLl-hf From (redirected): https://drive.google.com/uc?id=1lMC00LhK1HMoERpySn5BR5V0U9jLl-hf&confirm=t&uuid=8fb325ca-b4bc-4afc-8998-985a30fb0d44 To: /content/datasets.zip 100%|██████████| 264M/264M [00:04<00:00, 54.5MB/s]
✅ Archivo descargado con éxito. Procediendo a descomprimir...
label_paths = ["/content/datasets/datasets/labels/train", "/content/datasets/datasets/labels/val"]
for path in label_paths:
for file in os.listdir(path):
if file.endswith(".txt"):
file_path = os.path.join(path, file)
with open(file_path, "r") as f:
lines = f.readlines()
new_lines = []
for line in lines:
parts = line.split()
class_id = int(parts[0])
# Si la clase es mayor a 1, corregirla a 1 (casco)
if class_id > 1:
parts[0] = "1"
new_lines.append(" ".join(parts) + "\n")
with open(file_path, "w") as f:
f.writelines(new_lines)
print("✅ Todas las etiquetas inválidas han sido corregidas.")
✅ Todas las etiquetas inválidas han sido corregidas.
# Creacion de archivo data.yaml
data = {
'path': './datasets', # Ruta al dataset clonado
'train': 'images/train',
'val': 'images/val',
'nc': 2,
'names': ['sin_casco', 'casco'] # Modifica según tus clases
}
with open('/content/datasets/datasets/data.yaml', 'w') as file:
yaml.dump(data, file,
default_flow_style=False,
sort_keys=False)
YOLOv11, desarrollado por Ultralytics y presentado en 2024, es la última evolución de la familia YOLO, mejorando la precisión y velocidad en la detección de objetos en tiempo real. Con una extracción de características más avanzada y optimización para diversos entornos, es ideal para aplicaciones en dispositivos edge, la nube y sistemas con GPU. Esta versión tenía como objetivo mejorar tanto la velocidad como la precisión de las tareas de detección de objetos en comparación con sus predecesoras.
El objetivo de este trabajo es usar este modelo para detección de imágenes de trabajadores usando casco en entornos donde es obligatorio.
Se procede a realizar el entrenamiento del modelo, el cual, se realiza mediante un comando que contiene los siguientes parámetros:
A continuación, ejecutaremos el comando para entrenar el modelo. Repasemos los indicadores importantes en el siguiente comando:
data: Ruta del archivo YAML del conjunto de datos.
img: Resolución de las imágenes. De forma predeterminada es 640×640.
epochs: Numero de iteraciones completas sobre el conjunto de datos durante el entrenamiento.
plots: Habilita la generacion y almacenamiento de graficos relacionados con el entrenamiento.
model = YOLO("yolo11n.pt")
results = model.train(data="/content/datasets/datasets/data.yaml", epochs=90,imgsz=640, plots=True,batch=64, lr0=0.0005, optimizer="AdamW", patience=15)
Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...
100%|██████████| 5.35M/5.35M [00:00<00:00, 296MB/s]
Ultralytics 8.3.81 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB)
engine/trainer: task=detect, mode=train, model=yolo11n.pt, data=/content/datasets/datasets/data.yaml, epochs=90, time=None, patience=15, batch=64, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=AdamW, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.0005, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train
Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...
100%|██████████| 755k/755k [00:00<00:00, 130MB/s]
Overriding model.yaml nc=80 with nc=2
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 6640 ultralytics.nn.modules.block.C3k2 [32, 64, 1, False, 0.25]
3 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
4 -1 1 26080 ultralytics.nn.modules.block.C3k2 [64, 128, 1, False, 0.25]
5 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
6 -1 1 87040 ultralytics.nn.modules.block.C3k2 [128, 128, 1, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 346112 ultralytics.nn.modules.block.C3k2 [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 249728 ultralytics.nn.modules.block.C2PSA [256, 256, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
13 -1 1 111296 ultralytics.nn.modules.block.C3k2 [384, 128, 1, False]
14 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
15 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
16 -1 1 32096 ultralytics.nn.modules.block.C3k2 [256, 64, 1, False]
17 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
18 [-1, 13] 1 0 ultralytics.nn.modules.conv.Concat [1]
19 -1 1 86720 ultralytics.nn.modules.block.C3k2 [192, 128, 1, False]
20 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
21 [-1, 10] 1 0 ultralytics.nn.modules.conv.Concat [1]
22 -1 1 378880 ultralytics.nn.modules.block.C3k2 [384, 256, 1, True]
23 [16, 19, 22] 1 431062 ultralytics.nn.modules.head.Detect [2, [64, 128, 256]]
YOLO11n summary: 181 layers, 2,590,230 parameters, 2,590,214 gradients, 6.4 GFLOPs
Transferred 448/499 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/
Freezing layer 'model.23.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
train: Scanning /content/datasets/datasets/labels/train... 5269 images, 0 backgrounds, 0 corrupt: 100%|██████████| 5269/5269 [00:03<00:00, 1464.24it/s]
train: New cache created: /content/datasets/datasets/labels/train.cache albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
/usr/local/lib/python3.11/dist-packages/albumentations/__init__.py:28: UserWarning: A new version of Albumentations is available: '2.0.5' (you have '2.0.4'). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.
check_for_updates()
val: Scanning /content/datasets/datasets/labels/val... 1766 images, 0 backgrounds, 0 corrupt: 100%|██████████| 1766/1766 [00:01<00:00, 1231.29it/s]
val: New cache created: /content/datasets/datasets/labels/val.cache Plotting labels to runs/detect/train/labels.jpg... optimizer: AdamW(lr=0.0005, momentum=0.937) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.0005), 87 bias(decay=0.0) TensorBoard: model graph visualization added ✅ Image sizes 640 train, 640 val Using 8 dataloader workers Logging results to runs/detect/train Starting training for 90 epochs... Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/90 8.66G 1.498 1.758 1.227 143 640: 100%|██████████| 83/83 [00:20<00:00, 4.10it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:07<00:00, 1.94it/s]
all 1766 6808 0.159 0.781 0.467 0.301
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/90 8.63G 1.325 0.9449 1.126 130 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.31it/s]
all 1766 6808 0.897 0.847 0.929 0.573
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
3/90 8.6G 1.31 0.8528 1.114 127 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.91 0.874 0.931 0.601
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
4/90 9.1G 1.273 0.8171 1.099 169 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.918 0.861 0.933 0.6
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
5/90 8.99G 1.281 0.7942 1.103 82 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.27it/s]
all 1766 6808 0.932 0.896 0.946 0.623
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
6/90 8.83G 1.258 0.7744 1.096 152 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.34it/s]
all 1766 6808 0.91 0.881 0.93 0.591
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
7/90 8.73G 1.237 0.7355 1.082 138 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.936 0.902 0.953 0.635
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
8/90 8.86G 1.235 0.7291 1.084 145 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.937 0.902 0.951 0.635
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
9/90 8.71G 1.221 0.7078 1.072 129 640: 100%|██████████| 83/83 [00:18<00:00, 4.39it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.33it/s]
all 1766 6808 0.937 0.898 0.953 0.631
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
10/90 9.03G 1.208 0.6949 1.071 161 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.33it/s]
all 1766 6808 0.942 0.902 0.956 0.644
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
11/90 8.6G 1.22 0.6952 1.074 150 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.30it/s]
all 1766 6808 0.937 0.902 0.956 0.648
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
12/90 8.83G 1.202 0.6828 1.066 109 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.936 0.901 0.951 0.632
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
13/90 8.73G 1.203 0.6791 1.067 94 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.936 0.917 0.957 0.638
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
14/90 8.87G 1.203 0.6758 1.062 108 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.932 0.909 0.953 0.653
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
15/90 8.86G 1.199 0.6668 1.06 163 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.938 0.906 0.956 0.641
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
16/90 8.6G 1.189 0.6646 1.057 179 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.939 0.909 0.958 0.651
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
17/90 9.11G 1.181 0.6574 1.056 118 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.937 0.915 0.961 0.652
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
18/90 8.99G 1.176 0.6477 1.054 124 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.942 0.908 0.957 0.649
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
19/90 8.66G 1.167 0.6405 1.053 101 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.34it/s]
all 1766 6808 0.938 0.92 0.963 0.658
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
20/90 8.62G 1.17 0.6411 1.05 197 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.928 0.906 0.956 0.645
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
21/90 8.66G 1.167 0.6371 1.051 133 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.94 0.905 0.957 0.654
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
22/90 8.73G 1.171 0.6342 1.047 163 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.32it/s]
all 1766 6808 0.942 0.913 0.96 0.648
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
23/90 8.67G 1.159 0.6267 1.045 118 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.935 0.922 0.961 0.661
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
24/90 9.01G 1.166 0.6233 1.047 121 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.934 0.917 0.962 0.663
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
25/90 9.3G 1.157 0.6198 1.043 152 640: 100%|██████████| 83/83 [00:18<00:00, 4.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.947 0.918 0.963 0.659
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
26/90 8.87G 1.147 0.6132 1.042 117 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.33it/s]
all 1766 6808 0.949 0.913 0.962 0.659
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
27/90 8.83G 1.148 0.6175 1.042 142 640: 100%|██████████| 83/83 [00:18<00:00, 4.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.29it/s]
all 1766 6808 0.945 0.917 0.964 0.657
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
28/90 8.74G 1.154 0.6126 1.046 86 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.943 0.913 0.963 0.662
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
29/90 8.9G 1.15 0.613 1.039 142 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.946 0.92 0.964 0.66
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
30/90 8.61G 1.146 0.6041 1.038 154 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.941 0.918 0.965 0.658
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
31/90 8.63G 1.133 0.5993 1.033 216 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.942 0.919 0.961 0.653
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
32/90 8.88G 1.139 0.5993 1.036 187 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.943 0.92 0.962 0.666
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
33/90 8.63G 1.131 0.5927 1.033 110 640: 100%|██████████| 83/83 [00:18<00:00, 4.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.94 0.918 0.963 0.662
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
34/90 8.63G 1.13 0.5897 1.034 148 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.944 0.917 0.962 0.66
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
35/90 8.82G 1.125 0.5904 1.03 103 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.32it/s]
all 1766 6808 0.941 0.919 0.964 0.667
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
36/90 8.64G 1.123 0.5869 1.034 130 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.941 0.924 0.961 0.663
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
37/90 9.1G 1.126 0.5887 1.029 160 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.945 0.92 0.965 0.67
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
38/90 8.54G 1.124 0.5773 1.028 149 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.947 0.923 0.965 0.672
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
39/90 8.85G 1.122 0.5791 1.024 137 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.942 0.922 0.962 0.668
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
40/90 8.74G 1.12 0.5694 1.024 155 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.941 0.924 0.963 0.666
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
41/90 8.66G 1.108 0.5699 1.021 149 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.943 0.925 0.963 0.663
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
42/90 8.88G 1.111 0.5629 1.021 196 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.945 0.924 0.963 0.664
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
43/90 8.67G 1.103 0.5629 1.018 142 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.947 0.922 0.963 0.671
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
44/90 8.92G 1.107 0.5613 1.024 129 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.946 0.923 0.964 0.674
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
45/90 8.61G 1.102 0.5584 1.017 166 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.947 0.917 0.963 0.668
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
46/90 8.63G 1.096 0.5511 1.016 108 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.946 0.924 0.963 0.664
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
47/90 8.9G 1.086 0.55 1.015 188 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.34it/s]
all 1766 6808 0.945 0.926 0.962 0.672
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
48/90 8.87G 1.099 0.5526 1.017 148 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.947 0.922 0.962 0.673
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
49/90 8.6G 1.085 0.5461 1.014 130 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.945 0.926 0.962 0.671
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
50/90 8.61G 1.088 0.545 1.015 149 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.947 0.924 0.965 0.669
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
51/90 9.09G 1.092 0.5462 1.014 147 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.947 0.926 0.963 0.674
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
52/90 9.28G 1.08 0.5389 1.009 143 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.95 0.92 0.965 0.672
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
53/90 8.64G 1.075 0.5372 1.005 145 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.948 0.919 0.964 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
54/90 8.6G 1.079 0.5355 1.01 117 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.948 0.924 0.963 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
55/90 8.63G 1.072 0.5306 1.005 117 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.34it/s]
all 1766 6808 0.945 0.925 0.964 0.673
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
56/90 8.85G 1.067 0.5254 1.006 117 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.942 0.929 0.966 0.672
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
57/90 8.63G 1.076 0.5306 1.006 128 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.951 0.918 0.965 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
58/90 9.04G 1.069 0.5264 1.005 165 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:06<00:00, 2.33it/s]
all 1766 6808 0.944 0.927 0.964 0.676
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
59/90 8.91G 1.064 0.5236 1.002 121 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.944 0.929 0.964 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
60/90 9.01G 1.07 0.5259 1.005 126 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.946 0.926 0.964 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
61/90 8.54G 1.059 0.5186 1.003 129 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.94 0.923 0.964 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
62/90 8.53G 1.054 0.5111 1.003 123 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.941 0.929 0.963 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
63/90 8.67G 1.058 0.5167 0.9998 130 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.946 0.927 0.965 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
64/90 9.08G 1.054 0.513 0.9981 118 640: 100%|██████████| 83/83 [00:18<00:00, 4.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.947 0.921 0.963 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
65/90 8.6G 1.047 0.5113 0.9961 173 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.942 0.927 0.964 0.677
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
66/90 9.01G 1.045 0.5101 0.9987 161 640: 100%|██████████| 83/83 [00:18<00:00, 4.43it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.948 0.921 0.965 0.677
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
67/90 8.63G 1.043 0.5061 0.9927 149 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.951 0.924 0.965 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
68/90 8.95G 1.047 0.5088 0.9968 168 640: 100%|██████████| 83/83 [00:18<00:00, 4.42it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.947 0.923 0.965 0.68
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
69/90 8.61G 1.04 0.5021 0.9936 157 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.948 0.926 0.965 0.679
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
70/90 9.13G 1.033 0.4983 0.9917 101 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.41it/s]
all 1766 6808 0.948 0.926 0.965 0.677
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
71/90 8.91G 1.034 0.4971 0.9918 133 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.949 0.922 0.964 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
72/90 8.63G 1.039 0.5007 0.9883 132 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.95 0.92 0.963 0.676
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
73/90 8.84G 1.033 0.4957 0.9917 140 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.949 0.921 0.963 0.679
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
74/90 8.88G 1.03 0.4944 0.9874 112 640: 100%|██████████| 83/83 [00:18<00:00, 4.46it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.944 0.924 0.963 0.679
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
75/90 9.13G 1.028 0.4908 0.9875 112 640: 100%|██████████| 83/83 [00:18<00:00, 4.47it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.947 0.924 0.964 0.681
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
76/90 8.72G 1.024 0.4884 0.9857 140 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.34it/s]
all 1766 6808 0.944 0.927 0.963 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
77/90 8.71G 1.019 0.4879 0.9874 101 640: 100%|██████████| 83/83 [00:18<00:00, 4.44it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.34it/s]
all 1766 6808 0.94 0.928 0.963 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
78/90 8.63G 1.024 0.4835 0.9839 174 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.95 0.922 0.963 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
79/90 8.6G 1.016 0.4852 0.9826 180 640: 100%|██████████| 83/83 [00:18<00:00, 4.41it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.40it/s]
all 1766 6808 0.952 0.92 0.964 0.679
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
80/90 8.61G 1.015 0.4816 0.985 93 640: 100%|██████████| 83/83 [00:18<00:00, 4.45it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.95 0.921 0.964 0.68
Closing dataloader mosaic
albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
81/90 8.53G 1.005 0.4388 0.9863 62 640: 100%|██████████| 83/83 [00:20<00:00, 4.01it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.36it/s]
all 1766 6808 0.947 0.924 0.962 0.675
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
82/90 8.53G 0.9889 0.4238 0.9786 97 640: 100%|██████████| 83/83 [00:18<00:00, 4.52it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.949 0.923 0.962 0.676
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
83/90 8.54G 0.9821 0.4214 0.9758 76 640: 100%|██████████| 83/83 [00:18<00:00, 4.53it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.952 0.919 0.962 0.677
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
84/90 8.43G 0.9812 0.4184 0.9784 67 640: 100%|██████████| 83/83 [00:18<00:00, 4.51it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.37it/s]
all 1766 6808 0.953 0.919 0.963 0.676
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
85/90 8.53G 0.9786 0.4185 0.9747 77 640: 100%|██████████| 83/83 [00:18<00:00, 4.55it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.35it/s]
all 1766 6808 0.949 0.921 0.962 0.677
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
86/90 8.53G 0.9741 0.4144 0.9721 95 640: 100%|██████████| 83/83 [00:18<00:00, 4.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.952 0.919 0.963 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
87/90 8.53G 0.9746 0.412 0.9736 72 640: 100%|██████████| 83/83 [00:18<00:00, 4.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.39it/s]
all 1766 6808 0.954 0.917 0.963 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
88/90 8.53G 0.9663 0.4086 0.9674 82 640: 100%|██████████| 83/83 [00:18<00:00, 4.53it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.955 0.919 0.962 0.677
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
89/90 8.54G 0.9666 0.4094 0.9694 95 640: 100%|██████████| 83/83 [00:18<00:00, 4.53it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.953 0.919 0.962 0.678
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
90/90 8.51G 0.9655 0.4095 0.9689 139 640: 100%|██████████| 83/83 [00:18<00:00, 4.54it/s]
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:05<00:00, 2.38it/s]
all 1766 6808 0.954 0.917 0.962 0.677
EarlyStopping: Training stopped early as no improvement observed in last 15 epochs. Best results observed at epoch 75, best model saved as best.pt.
To update EarlyStopping(patience=15) pass a new patience value, i.e. `patience=300` or use `patience=0` to disable EarlyStopping.
90 epochs completed in 0.635 hours. Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB Validating runs/detect/train/weights/best.pt... Ultralytics 8.3.81 🚀 Python-3.11.11 torch-2.5.1+cu124 CUDA:0 (NVIDIA A100-SXM4-40GB, 40507MiB) YOLO11n summary (fused): 100 layers, 2,582,542 parameters, 0 gradients, 6.3 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100%|██████████| 14/14 [00:08<00:00, 1.60it/s]
all 1766 6808 0.948 0.924 0.964 0.681
sin_casco 339 1803 0.941 0.935 0.97 0.69
casco 1604 5005 0.955 0.913 0.958 0.672
Speed: 0.1ms preprocess, 0.3ms inference, 0.0ms loss, 0.9ms postprocess per image
Results saved to runs/detect/train
Una vez finalizado el entrenamiento, los pesos del modelo se almacenarán en la carpeta /content/runs/detect/train/weights/best.pt. Por lo tanto, si se desea reutilizar el modelo, este archivo puede ser cargado y empleado para la clasificación sin necesidad de volver a entrenarlo con los mismos parámetros.
A continuación se muestran los resultados de la evaluación del modelo
model_afinado = YOLO('/content/runs/detect/train/weights/best.pt')
evaluation = model_afinado.val()
image_path = "/content/runs/detect/train/train_batch0.jpg"
display(Image.open(image_path))
def print_metrics(evaluation):
print("📊 Resultados de evaluación:")
print(f"🔹 mAP (IoU@0.5): {evaluation.box.maps[0]:.4f}")
print(f"🔹 mAP (IoU@0.5:0.95): {evaluation.box.maps[1]:.4f}")
print(f"🔹 Precisión: {evaluation.box.p.mean():.4f}")
print(f"🔹 Recall: {evaluation.box.r.mean():.4f}")
print(f"🔹 F1-score: {evaluation.box.f1.mean():.4f}")
print_metrics(evaluation)
📊 Resultados de evaluación: 🔹 mAP (IoU@0.5): 0.6902 🔹 mAP (IoU@0.5:0.95): 0.6725 🔹 Precisión: 0.9429 🔹 Recall: 0.9256 🔹 F1-score: 0.9341
print("Valores en p (precisión):", evaluation.box.p)
print("Valores en r (recall):", evaluation.box.r)
Valores en p (precisión): [ 0.93678 0.94909] Valores en r (recall): [ 0.93691 0.91429]
preds = model_afinado('/content/datasets/datasets/images/test', conf=0.3)
image 1/75 /content/datasets/datasets/images/test/000002.jpg: 384x640 3 cascos, 13.6ms image 2/75 /content/datasets/datasets/images/test/000004.jpg: 448x640 2 cascos, 10.8ms image 3/75 /content/datasets/datasets/images/test/000005.jpg: 480x640 3 cascos, 10.7ms image 4/75 /content/datasets/datasets/images/test/000006.jpg: 544x640 2 cascos, 10.7ms image 5/75 /content/datasets/datasets/images/test/000007.jpg: 480x640 5 cascos, 10.6ms image 6/75 /content/datasets/datasets/images/test/000008.jpg: 480x640 1 sin_casco, 10.0ms image 7/75 /content/datasets/datasets/images/test/000009.jpg: 448x640 2 cascos, 10.7ms image 8/75 /content/datasets/datasets/images/test/000010.png: 448x640 3 cascos, 10.0ms image 9/75 /content/datasets/datasets/images/test/000012.jpg: 448x640 2 cascos, 9.9ms image 10/75 /content/datasets/datasets/images/test/000013.jpg: 416x640 3 cascos, 10.4ms image 11/75 /content/datasets/datasets/images/test/000014.jpg: 480x640 1 casco, 10.5ms image 12/75 /content/datasets/datasets/images/test/000015.jpg: 448x640 2 cascos, 10.5ms image 13/75 /content/datasets/datasets/images/test/000016.jpg: 448x640 2 cascos, 9.8ms image 14/75 /content/datasets/datasets/images/test/000018.png: 480x640 1 casco, 11.1ms image 15/75 /content/datasets/datasets/images/test/000020.jpg: 448x640 2 cascos, 11.4ms image 16/75 /content/datasets/datasets/images/test/000022.jpg: 384x640 4 cascos, 10.5ms image 17/75 /content/datasets/datasets/images/test/000023.png: 448x640 1 casco, 10.3ms image 18/75 /content/datasets/datasets/images/test/000024.jpg: 640x448 1 casco, 10.2ms image 19/75 /content/datasets/datasets/images/test/000025.jpg: 480x640 1 casco, 10.3ms image 20/75 /content/datasets/datasets/images/test/000026.jpg: 448x640 1 casco, 10.3ms image 21/75 /content/datasets/datasets/images/test/000029.jpg: 480x640 1 casco, 10.2ms image 22/75 /content/datasets/datasets/images/test/000030.jpg: 640x640 2 cascos, 10.3ms image 23/75 /content/datasets/datasets/images/test/000031.jpg: 448x640 1 casco, 10.2ms image 24/75 /content/datasets/datasets/images/test/000037.jpg: 480x640 2 cascos, 10.5ms image 25/75 /content/datasets/datasets/images/test/000038.jpg: 448x640 1 casco, 10.2ms image 26/75 /content/datasets/datasets/images/test/000039.jpg: 448x640 2 cascos, 10.4ms image 27/75 /content/datasets/datasets/images/test/000042.jpg: 640x640 1 casco, 10.1ms image 28/75 /content/datasets/datasets/images/test/000043.jpg: 448x640 5 cascos, 10.5ms image 29/75 /content/datasets/datasets/images/test/000044.jpg: 384x640 4 cascos, 10.2ms image 30/75 /content/datasets/datasets/images/test/000045.jpg: 480x640 2 cascos, 10.3ms image 31/75 /content/datasets/datasets/images/test/000046.jpg: 384x640 1 sin_casco, 10 cascos, 10.3ms image 32/75 /content/datasets/datasets/images/test/000502.jpg: 384x640 3 cascos, 9.8ms image 33/75 /content/datasets/datasets/images/test/000504.jpg: 448x640 2 cascos, 10.3ms image 34/75 /content/datasets/datasets/images/test/000505.jpg: 480x640 3 cascos, 10.3ms image 35/75 /content/datasets/datasets/images/test/000506.jpg: 544x640 2 cascos, 10.2ms image 36/75 /content/datasets/datasets/images/test/000507.jpg: 480x640 5 cascos, 10.3ms image 37/75 /content/datasets/datasets/images/test/000508.jpg: 480x640 1 sin_casco, 9.7ms image 38/75 /content/datasets/datasets/images/test/000509.jpg: 448x640 2 cascos, 10.5ms image 39/75 /content/datasets/datasets/images/test/000510.jpg: 448x640 2 cascos, 10.1ms image 40/75 /content/datasets/datasets/images/test/000510.png: 448x640 3 cascos, 10.1ms image 41/75 /content/datasets/datasets/images/test/000512.jpg: 448x640 2 cascos, 9.9ms image 42/75 /content/datasets/datasets/images/test/000513.jpg: 416x640 3 cascos, 10.5ms image 43/75 /content/datasets/datasets/images/test/000514.jpg: 480x640 1 casco, 10.6ms image 44/75 /content/datasets/datasets/images/test/000516.jpg: 448x640 2 cascos, 10.5ms image 45/75 /content/datasets/datasets/images/test/000517.jpg: 448x640 2 cascos, 9.8ms image 46/75 /content/datasets/datasets/images/test/000518.jpg: 480x640 2 cascos, 10.3ms image 47/75 /content/datasets/datasets/images/test/000519.jpg: 448x640 1 sin_casco, 2 cascos, 10.4ms image 48/75 /content/datasets/datasets/images/test/000519.png: 480x640 1 casco, 10.8ms image 49/75 /content/datasets/datasets/images/test/000522.jpg: 448x640 2 cascos, 11.9ms image 50/75 /content/datasets/datasets/images/test/000524.jpg: 384x640 4 cascos, 10.9ms image 51/75 /content/datasets/datasets/images/test/000525.jpg: 384x640 4 cascos, 10.6ms image 52/75 /content/datasets/datasets/images/test/000525.png: 448x640 1 casco, 11.0ms image 53/75 /content/datasets/datasets/images/test/000526.jpg: 640x448 1 casco, 10.9ms image 54/75 /content/datasets/datasets/images/test/000527.jpg: 480x640 1 casco, 11.3ms image 55/75 /content/datasets/datasets/images/test/000527.png: 480x640 1 casco, 11.1ms image 56/75 /content/datasets/datasets/images/test/000529.jpg: 416x640 2 cascos, 11.5ms image 57/75 /content/datasets/datasets/images/test/000531.jpg: 480x640 1 casco, 13.2ms image 58/75 /content/datasets/datasets/images/test/000532.jpg: 640x640 2 cascos, 11.4ms image 59/75 /content/datasets/datasets/images/test/000533.jpg: 448x640 1 casco, 11.6ms image 60/75 /content/datasets/datasets/images/test/000535.jpg: 480x640 2 cascos, 10.6ms image 61/75 /content/datasets/datasets/images/test/000541.jpg: 480x640 2 cascos, 10.0ms image 62/75 /content/datasets/datasets/images/test/000542.jpg: 448x640 1 casco, 10.4ms image 63/75 /content/datasets/datasets/images/test/000543.jpg: 448x640 2 cascos, 9.7ms image 64/75 /content/datasets/datasets/images/test/000546.jpg: 640x640 1 casco, 10.1ms image 65/75 /content/datasets/datasets/images/test/000547.jpg: 448x640 5 cascos, 10.6ms image 66/75 /content/datasets/datasets/images/test/000548.jpg: 448x640 1 casco, 9.9ms image 67/75 /content/datasets/datasets/images/test/000550.jpg: 480x640 2 cascos, 10.2ms image 68/75 /content/datasets/datasets/images/test/000551.jpg: 384x640 1 sin_casco, 10 cascos, 10.3ms image 69/75 /content/datasets/datasets/images/test/000552.jpg: 480x640 2 cascos, 10.2ms image 70/75 /content/datasets/datasets/images/test/000554.jpg: 448x640 2 cascos, 10.3ms image 71/75 /content/datasets/datasets/images/test/000557.jpg: 448x640 1 casco, 9.7ms image 72/75 /content/datasets/datasets/images/test/000559.jpg: 640x640 2 sin_cascos, 3 cascos, 10.1ms image 73/75 /content/datasets/datasets/images/test/000562.jpg: 480x640 2 cascos, 10.5ms image 74/75 /content/datasets/datasets/images/test/000563.jpg: 448x640 1 casco, 10.3ms image 75/75 /content/datasets/datasets/images/test/000565.jpg: 448x640 2 cascos, 9.7ms Speed: 2.4ms preprocess, 10.5ms inference, 1.2ms postprocess per image at shape (1, 3, 448, 640)
preds[1].show()
preds[11].show()
preds[21].show()
preds[31].show()
preds[67].show()
